About the role
We are seeking an experienced Applied AI Engineer with proven experience embedding AI/ML technologies into enterprise software. You will play a critical role in building our MVP from the ground up, working closely with US and India teammates to design, develop, and ship features end-to-end.
This is a unique opportunity for someone who thrives in an early-stage environment, enjoys the energy of building all new AI-enabled applications, and is motivated to shape both the product and the engineering culture
Responsibilities:
- Full-stack delivery: Design, develop, and ship features across the data stack and apply enterprise SaaS best practices.
- AI integration: Transform user workflows to accelerate and simplify client tasks with AI and ML tooling.
- Product collaboration: Partner with Product and Design to translate customer workflows into performant, elegant product experiences.
- Technical quality: Own testing, reliability, observability, and security for the models and features you ship.
- Architecture and reviews: Contribute to system design and code reviews; help set engineering patterns that scale for AI.
- Customer focus: Engage with external users to validate assumptions and iterate quickly.
- Culture and collaboration: Model high ownership, humility, and teamwork; contribute to a strong engineering team culture and collaboration.
Requirements:
- 5+ years of professional experience in engineering (enterprise SaaS preferred) and/or AI/ML products.
- Excellent communicator, comfortable collaborating across cultures and time zones.
- Hands- on experience integrating LLMs, vector databases, RAG pipelines, and AI-powered workflows.
- Solid data expertise: relational + NoSQL databases, data modelling, indexing, and performance optimisation.
- Proficiency with cloud platforms (AWS/Azure/GCP), containerization, CI/CD, and infrastructure-as-code.
- Track record of delivering in fast-moving, ambiguous environments.
Bonus:
- Experience with energy sector workflows (utility-scale renewables, data centres, grid infrastructure, etc. ).
- Strong proficiency in React + TypeScript on the front end, and Python/Node.js on the back end.
- Background in geospatial data, enterprise document systems, or integrations.
- Familiarity with secure-by-default enterprise SaaS practices.